Index sorting systems and methods

ABSTRACT

Features for a quantitative biological data analysis system are described. Index sort analysis can be performed using sort electronics on a flow cytometer or other particle analysis system or after an experiment using a workstation. The sort electronics on the cytometer or other particle analysis system may generate sort decision information for the events. The sort decision information may be transmitted from the sort electronics to the workstation that may differ from the sort identified by the workstation. The feature fuse the gate information with the sorting decision information to provide an accurate representation of the sorting.

CROSS-REFERENCE TO RELATED APPLICATIONS

The application claims priority to U.S. Provisional Application No.62/518512, filed on Jun. 12, 2017, which is incorporated by reference inits entirety. Any and all priority claims identified in the ApplicationData Sheet, or any correction thereto, are also hereby incorporated byreference under 37 C.F.R. § 1.57.

FIELD

The present application generally relates to index sorting systems andmethods, specifically index sorting systems and methods for analysis ofquantitative biological event data.

BACKGROUND

Particle analyzers, such as flow and scanning cytometers, are analyticaltools that enable the characterization of particles on the basis ofoptical parameters such as light scatter and fluorescence. In a flowcytometer, for example, particles, such as molecules, analyte-boundbeads, or individual cells, in a fluid suspension are passed by adetection region in which the particles are exposed to an excitationlight, typically from one or more lasers, and the light scattering andfluorescence properties of the particles are measured. Particles orcomponents thereof typically are labeled with fluorescent dyes tofacilitate detection. A multiplicity of different particles orcomponents may be simultaneously detected by using spectrally distinctfluorescent dyes to label the different particles or components. In someimplementations, a multiplicity of photodetectors, one for each of thescatter parameters to be measured, and one for each of the distinct dyesto be detected are included in the analyzer. The data obtained comprisethe signals measured for each of the light scatter parameters and thefluorescence emissions.

Cytometers may further comprise means for recording the measured dataand analyzing the data. For example, data storage and analysis may becarried out using a computer connected to the detection electronics. Forexample, the data can be stored in tabular form, where each rowcorresponds to data for one particle, and the columns correspond to eachof the measured parameters. The use of standard file formats, such as an“FCS” file format, for storing data from a flow cytometer facilitatesanalyzing data using separate programs and/or machines. Using currentanalysis methods, the data typically are displayed in 2-dimensional (2D)plots for ease of visualization, but other methods may be used tovisualize multidimensional data.

The parameters measured using a flow cytometer typically include theexcitation light that is scattered by the particle along a mostlyforward direction, referred to as forward scatter (FSC), the excitationlight that is scattered by the particle in a mostly sideways direction,referred to as side scatter (SSC), and the light emitted fromfluorescent molecules in one or more channels (range of frequencies) ofthe spectrum, referred to as FL1, FL2, etc., or by the fluorescent dyethat is primarily detected in that channel. Different cell types can beidentified by the scatter parameters and the fluorescence emissionsresulting from labeling various cell proteins with dye-labeledantibodies.

Both flow and scanning cytometers are commercially available from, forexample, BD Biosciences (San Jose, Calif.). Flow cytometry is describedin, for example, Landy et al. (eds.), Clinical Flow Cytometry, Annals ofthe New York Academy of Sciences Volume 677 (1993); Bauer et al. (eds.),Clinical Flow Cytometry: Principles and Applications, Williams & Wilkins(1993); Ormerod (ed.), Flow Cytometry: A Practical Approach, OxfordUniv. Press (1994); Jaroszeski et al. (eds.), Flow Cytometry Protocols,Methods in Molecular Biology No. 91, Humana Press (1997); and PracticalShapiro, Flow Cytometry, 4th ed., Wiley-Liss (2003); all incorporatedherein by reference. Fluorescence imaging microscopy is described in,for example, Pawley (ed.), Handbook of Biological Confocal Microscopy,2nd Edition, Plenum Press (1989), incorporated herein by reference.

The data obtained from an analysis of cells (or other particles) bymulti-color flow cytometry are multidimensional, wherein each cellcorresponds to a point in a multidimensional space defined by theparameters measured. Populations of cells or particles are identified asclusters of points in the data space. The identification of clustersand, thereby, populations can be carried out manually by drawing a gatearound a population displayed in one or more 2-dimensional plots,referred to as “scatter plots” or “dot plots,” of the data.Alternatively, clusters can be identified, and gates that define thelimits of the populations, can be determined automatically. Examples ofmethods for automated gating have been described in, for example, U.S.Pat. Nos. 4,845,653; 5,627,040; 5,739,000; 5,795,727; 5,962,238;6,014,904; and 6,944,338; and U.S. Pat. Pub. No. 2012/0245889, eachincorporated herein by reference.

Flow cytometry is a valuable method for the analysis and isolation ofbiological particles such as cells and constituent molecules. As such ithas a wide range of diagnostic and therapeutic applications. The methodutilizes a fluid stream to linearly segregate particles such that theycan pass, single file, through a detection apparatus. Individual cellscan be distinguished according to their location in the fluid stream andthe presence of detectable markers. Thus, a flow cytometer can be usedto produce a diagnostic profile of a population of biological particles.

Isolation of biological particles has been achieved by adding a sortingor collection capability to flow cytometers. Particles in a segregatedstream, detected as having one or more desired characteristics, areindividually isolated from the sample stream by mechanical or electricalremoval. This method of flow sorting has been used to sort cells ofdifferent types, to separate sperm bearing X and Y chromosomes foranimal breeding, to sort chromosomes for genetic analysis, and toisolate particular organisms from complex biological population.

SUMMARY

One general aspect includes a computer-implemented method performedunder control of one or more electronic processors. Thecomputer-implemented method includes receiving, at a workstation from amemory, experiment information identifying: a first sort gate; a firstplate destination for particles identified within the first sort gate ona collection plate; a second sort gate; and a second plate destinationfor particles identified within the second sort gate on the collectionplate. The computer-implemented method includes generating, at theworkstation, a first index sort inclusion bitmap identifying first platelocations on the collection plate using the first sort gate. Thecomputer-implemented method includes generating, at the workstation, asecond index sort inclusion bitmap identifying second plate locations onthe collection plate using the second sort gate. Thecomputer-implemented method includes receiving, at the workstation,event data for the particles, the event data including for each particlea fluorescent parameter value and a sort decision of a sorting device.The computer-implemented method includes determining, via theworkstation, that an individual fluorescent parameter value for anindividual particle included in the particles is within the first sortgate. The computer-implemented method includes associating theindividual particle with the first sort gate for presentation via a userinterface. The computer-implemented method includes determining, via theworkstation, that an individual sort decision for the individualparticle corresponds to the second sort gate based at least in part on acomparison between the individual sort decision and the second indexsort inclusion bitmap. The computer-implemented method includesassociating the individual particle with the second sort gate forpresentation via the user interface.

Implementations of the computer-implemented method may include one ormore of the following features. The computer-implemented method wherethe sort decision is generated by a sorting device configured to sortthe particles into the collection plate based at least in part on theexperiment information. The computer-implemented method furtherincluding: receiving, at the workstation, a geometric shape defining thefirst sort gate, where the geometric shape is pixelated. Thecomputer-implemented method further including: receiving, at theworkstation, a request to adjust an axis for a geometric shape definingthe first sort gate, where the geometric shape, after the axis isadjusted, is pixelated. The computer-implemented method where therequest includes a binomial transformation to adjust the axis. Thecomputer-implemented method where the first sort gate identifies a setof particles having parameter values distinct from another set ofparticles identified by the second sort gate.

One general aspect includes an apparatus including: a particle analyzer(such as a flow cytometer) configured to acquire quantitative biologicalevent data (e.g., flow cytometric event data). The apparatus alsoincludes one or more processors in communication with the particleanalyzer, the one or more processors configured to: receive, from amemory, experiment information identifying: a first sort gate; a firstplate destination for particles identified within the first sort gate; asecond sort gate; and a second plate destination for particlesidentified within the second sort gate. The one or more processors areconfigured to generate a first index sort inclusion bitmap uniquelyidentifying the first sort gate. The one or more processors areconfigured to generate a second index sort inclusion bitmap uniquelyidentifying and the second sort gate. The one or more processors areconfigured to receive, from the particle analyzer, event data for theparticles, the event data including for each particle a fluorescentparameter value and a sort decision. The one or more processors areconfigured to determine that an individual fluorescent parameter valuefor an individual particle included in the particles is within the firstsort gate. The one or more processors are configured to associate theindividual particle with the first sort gate for presentation via a userinterface. The one or more processors are configured to determine thatan individual sort decision for the individual particle corresponds tothe second sort gate based at least in part on a comparison between theindividual sort decision and the second index sort inclusion bitmap. Theone or more processors are configured to associate the individualparticle with the second sort gate for presentation via the userinterface.

Implementations of the apparatus may include one or more of thefollowing features. The apparatus where the sort decision is generatedby the particle analyzer, where the particle analyzer is configured tosort the particles into the plate based at least in part on theexperiment information. The apparatus where the one or more processorsare further configured to: receive a geometric shape defining the firstsort gate, where the geometric shape is pixelated. The apparatus wherethe one or more processors are further configured to: receive a requestto adjust an axis for a geometric shape defining the first sort gate,where the geometric shape, after the axis is adjusted, is pixelated. Theapparatus where the request includes a non-linear transformation toadjust the axis. The apparatus where the first sort gate identifies aset of particles having parameter values distinct from another set ofparticles identified by the second sort gate. The apparatus where theone or more processors are further configured to: cause display of agraphic representation of the plate, the graphic representation of theplate including a first area representing the second plate destination;and cause display of a graphic indicator for the individual particlewithin the first area.

One general aspect includes a computer-readable medium having storedthereon instructions which when executed by a processor of a device,cause the device to at least: receive, from a memory, experimentinformation identifying: a first sort gate; a first plate destinationfor particles identified within the first sort gate; a second sort gate;and a second plate destination for particles identified within thesecond sort gate. Instructions are also included to generate a firstindex sort inclusion value uniquely identifying the first sort gate.Instructions are also included to generate a second index sort inclusionvalue uniquely identifying and the second sort gate. Instructions arealso included to receive, from a particle analyzer, event data for theparticles, the event data including for each particle a fluorescentparameter value and a sort decision. Instructions are also included todetermine that an individual fluorescent parameter value for anindividual particle included in the particles is within the first sortgate. Instructions are also included to associate the individualparticle with the first sort gate for presentation via a user interface.Instructions are also included to determine that an individual sortdecision for the individual particle corresponds to the second sort gatebased at least in part on a comparison between the individual sortdecision and the second index sort inclusion value. Instructions arealso included to associate the individual particle with the second sortgate for presentation via the user interface.

Implementations of the computer-readable medium may include one or moreof the following features. The computer-readable medium where the sortdecision is generated by a sorting device configured to sort theparticles into the plate based at least in part on the experimentinformation. The computer-readable medium further having stored thereoninstructions which when executed by the processor of the device, causethe device to at least to: receive a geometric shape defining the firstsort gate, where the geometric shape is pixelated. The computer-readablemedium further having stored thereon instructions which when executed bythe processor of the device, cause the device to at least to: receive arequest to adjust an axis for a geometric shape defining the first sortgate, where the geometric shape, after the axis is adjusted, ispixelated. The computer-readable medium where the request includes anon-linear transformation to adjust the axis. The computer-readablemedium where the first sort gate identifies a set of particles havingparameter values distinct from another set of particles identified bythe second sort gate. The computer-readable medium further having storedthereon instructions which when executed by the processor of the device,cause the device to at least to: cause display of presentation of agraphic representation of the plate, the graphic representation of theplate including a first area representing the second plate destination;and cause presentation of a graphic indicator for the individualparticle within the first area.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic drawing of a cell sorter system 100, in accordancewith one embodiment presented herein.

FIG. 2 illustrates a user interface for index sorting.

FIG. 3 illustrates an example of pixilation error for a portion of agate for defining a sort location.

FIG. 4 is a flow diagram illustrating an example method for fused gatingfor index sorting.

FIG. 5 shows a functional block diagram for one example of a graphicscontrol system for that may implement one or more of the featuresdescribed.

DETAILED DESCRIPTION

A common flow sorting technique utilizes drop sorting in which a fluidstream containing linearly segregated particles is broken into drops andthe drops containing particles of interest are electrically charged anddeflected into a collection tube by passage through an electric field.Current drop sorting systems are capable of forming drops at a rate of100,000 drops/second in a fluid stream that is passed through a nozzlehaving a diameter less than 100 micrometers. Drop sorting requires thatthe drops break off from the stream at a fixed distance from the nozzletip. The distance is normally on the order of a few millimeters from thenozzle tip and can be maintained for an unperturbed fluid stream byoscillating the nozzle tip at a predefined frequency.

Typically, the linearly segregated particles in the stream arecharacterized as they pass through an observation point situated justbelow the nozzle tip. Once a particle is identified as meeting one ormore desired criteria, the time at which it will reach the dropbreak-off point and break from the stream in a drop can be predicted.Ideally, a brief charge is applied to the fluid stream just before thedrop containing the selected particle breaks from the stream and thengrounded immediately after the drop breaks off. The drop to be sortedmaintains an electrical charge as it breaks off from the fluid stream,and all other drops are left uncharged. The charged drop is deflectedsideways from the downward trajectory of the other drops by anelectrical field and collected in a sample tube. The uncharged dropsfall directly into a drain.

FIG. 1 is a schematic drawing of a cell sorter system 100, in accordancewith one embodiment presented herein. As shown in FIG. 1, a dropformation transducer (e.g., piezo-oscillator) 102 is coupled to a fluidconduit, such as nozzle 101. Within nozzle 101, sheath fluid 104hydrodynamically focuses a sample fluid 106 into a stream 108. Withinstream 108, particles (e.g., cells) are lined up in single file to crossa laser-stream intersect 110 (e.g., the LJI), irradiated by anirradiation source (e.g., laser) 112. Vibration of piezo-oscillator 102causes stream 108 to break into a plurality of drops 109.

In operation, an event detector 114 identifies when a particle ofinterest (or cell of interest) crosses laser-stream intersect 110. Eventdetector 114 feeds into timing circuit 128, which in turn feeds intoflash charge circuit 130. At the drop break off point, informed by atimed drop delay (At), a flash charge is applied to the stream such thatthe drop of interest carries a charge. The charged drop can then besorted by activating deflection plates (not shown) to deflect the dropinto a collection tube or a multi-well sample plate where a well may beassociated with drops of particular interest. As shown in FIG. 1,however, the drops are collected in a drain receptacle 138.

Drop boundary detector 116 serves to automatically determine the phaseof the drop drive signal when a particle of interest passes thelaser-stream intersect 110. An exemplary drop boundary detector isdescribed in U.S. Pat. No. 7,679,039, which is incorporated herein byreference in its entirety. Drop boundary detector 116 allows theinstrument to accurately calculate the place of each detected particlein a drop. Drop boundary detector 116 feeds into an amplitude signal 120and phase 118 signal, which in turn feeds (via amplifier 122) into anamplitude control circuit 126 and/or frequency control circuit 124.Amplitude control circuit 126 and/or frequency control circuit 124, inturn, controls piezo-oscillator 102.

Cell sorter system 100 further includes a current-to-voltage converter(CVC) 134 coupled to the drain receptacle 138. CVC 134 is configured todetect the presence of a charged particle entering the drain receptacle138. Resistor 136 sets the volts-per-amp of CVC 134, and provides avoltage that is proportional to current observed at the drain receptacle138. Drain current is measured in circuit unit 132 and is provided to aprocessor 140. Processor 140 then feeds into frequency control circuit124.

In some implementations, sort electronics (e.g., the drop boundarydetector 116, the event detector 114, the processor 140) may be coupledwith a memory configured to store the detected events and a sortdecision based thereon. This information may be included in the eventdata for a particle. However, as discussed herein, the sorting decisiongenerated by the sort electronics may be different than a sortidentified by a workstation based on gates defined for an experiment. Asthe analysis of event data can be performed on a workstation, this maycause a disconnect between the cell sort information generated by theworkstation and the actual sorting for a plate.

In flow cytometry sorting, the use of index-sorting implies additionalinformation is available that links the individual cell events to theirdestination locations in a plate or slide holder. This information maybe used post-acquisition to do additional analysis of where cells arephysically located on a plate device. It also allows users to see wherethose cells are located on bi-variate plots. Current index sort productsoffer very limited interaction with the data during the analysis phase.

Index sorting is cell sorting where the sorting device may record thesort decision for each event (typically a cell or other particlesuspended in a flow stream) and the data is available for post sortanalysis. Typically index sorting is performed by detecting a propertyof a particle (such as color) and directing the particle into acollection plate. The plate may include several plate destinations(e.g., well locations). The soring may include directing the particle toa particular plate location (e.g., well) within the plate. The sortingdevice may record, in association with an identifier for the event, thedestination plate and/or well location. Each sorted event thus has allthe measurements from the detectors (PMTs, photodiodes) along with thewell location and sort destination. A user can examine a sorted cell'sdata and correlate it with subsequent operations on the plate (e.g.,gene expression derived from sequencing the sorted cells).

Index sorting analysis may require the display of both sorted andunsorted events with plots and statistics. The sorter may perform one ormore of: fluorescence compensation, axis transformation (e.g., binomialor other non-linear transformation), or region and gate classificationon each event.

A sort mode may be specified for a given sample. The sort mode includesthe parameters to control which events are sorted. For example, uponreceiving a sample at the sorting device, the sorting device may receivea sort mode to control what properties to use for sorting and wheredetected values for those properties should be sorted. Sort modes mayinclude a purity mode which may configure the cell sorter to ensure thatthe desired cell type and only the desired cell type are in the gate.Sort modes may include single cell mode which may configure the cellsorter to ensure that only a single cell is within the droplet. Sincethere can be uncertainty around the drop boundary of which droplet acell may be in, often following drops are not sorted even if they couldbe. For single cell sorting, this may be desirable for certainexperiments such as genomic based assays where users may want tocorrelate gene expression with flow cytometry derived measurements. Insuch instances, if there were multiple cells within the well, therewould be ambiguity as to which cell the gene sequences came from. Thesort mode may configure the cell sorter by setting a mask that examineswhere events fall within the droplet, and surrounding droplets. Somesingle cell sort mode configurations may include a state machine orexamination of a queue of events that fall within a droplet.

In some implementations, a target gate may be used to identify events ofinterest. A target gate may be provided by selecting an area on a twodimensional plot. Events that are detected with property values withinthe selected area for the two dimensions are considered within thetarget gate and may be sorted to a particular location. An event may bewithin the target gate but under certain sorting modes (e.g., a purityor single cell mode), the event may not be sorted correctly such as ifanother event is within the same drop in the fluidic stream as theevent. In some implementations, this may be referred to as entrainmentor cohesion.

As part of recording the sort decisions, the sort electronics of thesorting device may transmit the sort destination along with the eventraw data. Event raw data may include a detected property for the event(e.g., reflected light values, fluorescence information, light scatterinformation, time of the event, a sequence number for the event, sortingdevice operational characteristics at the time the event was analyzed(e.g., temperature, flow rate, sort mode, etc.), or the like). For indexsorting, the current tray, plate, microscope slide, or other physicalmedium with spatially separated pools where drops including cells may bedeposited, coordinates of the location where a cell for a particularevent was deposited may also be transmitted.

The sort electronics also transmits the region classifications the sortclassification that the digital signal processor (DSP) made for eachevent. The region classifications may include a bit mask of the currentregions sent to the sort electronics.

A region may include a bitmap on a pair of transformed parameters (e.g.CD4-A log vs CD8-A log, or FSC-A linear vs SSC-A linear). Any eventwhere the pair of transformed parameters falls within the set bits ofthe bitmap is considered to be within the region.

A target gate may consists of a truth table consisting of thecombinations of regions and gates such that events having parameterscorresponding to the region are considered members (e.g., “within”) theassociated gate. Other methods of expressing membership of a gate arepossible (e.g. a list of regions if the logic consists of an ANDcombination, use of a postfix logic expression, etc.).

FIG. 2 illustrates a user interface for index sorting. The userinterface 200 includes a hierarchy 240 of gates which may be applied forsorting a sample. The hierarchy 240 may include a root node representingall events for a sample. The root node may then include one or morechildren nodes. Some child nodes may be selected for sorting. In FIG. 2,singlets with a scatter parameter 1 may be identified for sorting to aspecific location (e.g., well, plate, etc.). By activating a controlelement on the interface, nodes may be added or removed from thehierarchy 240. A node may also be associated with a sort location on aplate. Accordingly, the user interface 200 may be used to define thesort logic for an experiment. Once the experiment has been run andexperiment information (e.g., event data) received for an experiment,the user interface 200 may be used to present the event data. To helpvisualize the sorting, the user interface 200 may include arepresentation of a target plate 230. The target plate 230 may be agraphical element the provides a visual representation of the wells on aplate that will be used (or has been used) for an experiment. Whendefining sorting, before the experiment has been run, the target plate230 or portions thereof may be selectively activated to identify welllocations to associate with a gate. For example, a well in the targetplate 230 may be selected and then a node in the hierarchy 240 selectedto associate with the selected well.

Parameter gates may be specified using one or more population plots 210.At design time, an area of a population plot may be selected to define apopulation of interest. For example, a polygon may be drawn to identifyan area on a two dimensional plot corresponding to x, y parameter valuesof interest. As a specific example, cells having certain properties mayexpress a known range of parameters. To sort these cells, a selection ofthe known range may be received to establish a gate for the cells ofinterest.

Each well within a plate index sort may be assigned a sort gate. A platemay consist of all the same sort gate, or may contain multiple distinctsort gates. Each gate belongs to a gating hierarchy: there is a parentnode ‘All events’ for the experiment, and then the gates are children ofthe parent or other gates. For example, a gate for CD4 positive cellsmay consist of ‘All Events’ which has a child ‘Scatter’ which hasanother child ‘Singlets’ and then a final child ‘CD4 positive’. The gatehierarchy may include gates that are used for sorting and gates that areparents of sort gates, along with non-sorting gates. Some gates may beused to sort some wells, but not other wells.

The user interface 200 may include a summary section 220. The summarysection 220 may provide a summary of the event data for an experiment.The summary section 220 may be generated by comparing parameter data forevents included in the event data with gates associated with theexperiment such as those included in the hierarchy 240. The populationsshown in the summary section 220 may be associated with respectivecontrol elements that, when activated, cause the user interface 200 todisplay information associated with the selected population(s). Forexample, if the P1 parameter is selected, the population plots 210 mayrender only those events associated with the P1 parameter.

There may be finite capacity for regions within the sort electronics andregions that are part of the experiment but not used as part of a sortgate may not be transmitted to the sort electronics. These regions maybe used for quality control or other purposes. In some implementations,the sort may change sort gates for each plate well being sorted. Forexample, a workstation may be configured to determine that the supersetof sort gates requires fewer regions than are available through the sortelectronics. In such instances, it may be more efficient to transmit allsort regions prior to the experiment and then switch gates for eachwell. It may also be that the superset requires more regions than areavailable on the hardware, but by selection of initial sort regionstransmitted to the cell sorting device (e.g., flow cytometer), theworkstation can minimize the number of sort regions that are replacedwith regions from another sort gate when moving to wells with differentselected sort gates. For instance, typically there is a common subset ofsort regions across sort gates that can be applied. In someimplementations, the workstation may transmit non sorting regions to thecell sorting device and receive the cell sorting device's classificationof the sort region. The transmission of non-sorting regions may beselectively performed based at least in part on resource constraintssuch as time to transmit, available sort regions in electronics, or thelike.

Analysis of events off the sorting device such as through the userinterface 200 via a workstation or other device with more abundantresources. This allows the workstation to perform event analysis at ahigher precision than the sort electronics. For example, region bitmapsmay be represented on the workstation at a higher resolution than on thesorting device. Interval or rectangular regions defined via theworkstation may be evaluated directly by comparing coordinates ratherthan by mapping to a bitmap. Since an interval region generally comparesa parameter measurement to a lower and upper bound, this event analysison the workstation can be performed without recourse to a bitmap (or aparameter transformation such as logarithmic or bi-exponential).Performing a similar assessment for another measurement may provide asecond dimension for defining a rectangle. The bitmap may be consultedwhen a user rotates a rectangle, thereby changing the dimensions for thegate. In such instances, the workstation may fall back to identifyingthe gate from the bitmap.

For example, the selection of a gate using a graphical user interface isrequires translation from pixel space to parameter value space.Presenting an event faces a conversion from parameter value to pixelspace. Near the edge of a polygon, translation may incorrectly identifycertain cells due to pixilation error. Pixilation error generally refersto the phenomena whereby smooth curves cannot be rendered using pixels.As such, the gate may appear to a human eye to be smooth, but jaggedportions of the boundary line may exist.

This may be desirable for precision and accuracy, but the classificationof an event within a gate may differ between the workstation softwareand the sort electronics. This can be a problem for index sortingbecause the user may wish to know how the event was sorted and where itappears on one or more dot plots.

Although the discussion references rectangles, similar gate definitionmay be performed for other shapes. Thus, a variety of geometric shapescould be analyzed by comparing a computation rather than examining abitmap. For example, an event may be deemed to be within a circular gateif the sum of the squares of the x and y offsets from the center is lessthan the square of the radius.

The user may wish to visualize the sorted events against unsortedevents. The sort electronics may not provide useful classification dataon unsorted events—the event may be within the gate but not sorted dueto sort mode, or the event may not be within the gate but still appearin other populations of interest to the user that were not classified bythe sort electronics.

FIG. 3 illustrates an example of pixilation error for a portion of agate for defining a sort location. A population plot 300 for twoparameters (p1 and p2) is shown. The population plot 300 includes a gate302 which may define a sort population. Because the gate 302 may bedefined using a user interface, the nature of pixel based graphicsprevents the generation of true smooth polygons.

FIG. 3 includes a magnified view of a portion 350 of the gate 302. Theportion 350 shows how the gate 302 may be formed from a series ofstraight lines. (302-a, 302-b, and 302-c). These lines may be one pixelwide but when viewed at a non-magnified resolution, provide theimpression of a circular shape.

The cell parameters may be included in event data received from the flowcytometer. A cell parameter may include a fluorescent parameter valuemeasured for a cell or particle. As shown, the x, y parameter valueswhen rendered on a graphical user interface may lie at or on theboundary of the gate 302. For example, an event 352 may be associatedwith a parameter value that may lie partially on and partially insidethe gate 302. Another event 354 may lie partially on and partiallyoutside the gate 302. In such instances, it is unclear, based on theparameters and user defined gate, whether the cell should be consideredwithin the gate or outside the gate. Event 358 is clearly outside thegate 302, but event 360 presents another questionable case. If the gate302 were a true circular polygon, the event 360 may be outside the gatebut because of the pixelated rendering of the gate 30, the event 360 maybe judged by the workstation as being within the gate. This shows someof the ways pixilation error may manifest itself when presenting eventdata via a user interface.

The gate 320 may also define a population of interest to be sorted. Insuch instances, additional sort data may be available. For example, ifthe sorting electronics detected parameters for the event shown in FIG.3, it may refer to a lookup table generated from the user interface 200defined gate. The lookup table may translate the pixel locations of thegate boundary to parameter ranges. However, the translation may notrepresent the same resolution when applied in the sorting electronics.For example, the parameter data for an event may not fall neatly insideor outside a gate, as shown in FIG. 3. In some implementations, theseerrors may be referred to as in/out effects.

To solve these problems, the event classification from the sortelectronics and the workstation software classification may be combined.

One solution may be to have the workstation replicate the classificationprocess performed by the sort electronics exactly. The classificationprecision would be limited to that of the sorter. The sort decisionwould also need to be replicated, which requires having the position ofthe event within the droplet to be known. There would also be a numberof numerical problems, e.g. order of operations, use of lookup tablesrather than built-in transcendental functions such as natural logarithm.For example, the workstation may program the sorting electronics usingthe same method as it will use itself such as by generating a bitmapbased lookup table. However, the depth of the lookup table or generationof lookup table indices may be performed with improved precision. Insome sorting electronics implementations, the bitmap for a log-log plotmay waste around a third of the bitmap because a naive logarithm methodwas used without an affine transformation. In some implementations, thebottom of the log plot may appears around bin 50 which leavessubstantial sort information unused or underused.

Another solution to provide a more accurate assessment of event data maybe to fuse the sort decision information from the sort electronics withthe workstation software sort classification to produce accurateinformation for the sorted events. For example, the sort decisioninformation may be encoded by the sorting electronics as the sortdestination for the event, the well x and y coordinates within a singleword sent with the event measurements. A word generally refers to avalue included in the event measurement data.

To provide accurate information for sorted events, such as rendering avisualization user interface or generating analytical values for theevents, the workstation software sort classification may be ignored forthe sorted events. Instead, the sort electronics classification may beused. Any parent gates of the sort gate can also use the sortelectronics classification.

An Exemplary Process

FIG. 4 is a flow diagram illustrating an example method for fused gatingfor index sorting. The method 400 may be implemented in whole or in partby one or more of the devices described such as a workstation or otherflow cytometry device. The method 400 illustrates how sort gatesspecified via a workstation can be corrected to ensure the events areprocessed according to the actual sort decisions from a cell sorterrather than solely based on the detected parameters for an event. Forexample, an index sort bitmap may be generated for each sort gate andthe index sort bitmap may include an entry for each well. After the sortgate is evaluated by the workstation, the index sort data from theelectronics may be examined and if the corresponding bitmap entry forthe well coordinates is set and the event was sorted, then the gateclassification from the software is replaced. Another embodiment wouldbe to have the well coordinates look up a table of gates that areaffected.

A sort gate includes an inductive sort into any parent nodes in the sorthierarchy. Thus, even though there is only one ‘gate’ identified by thesort electronics per well, the analysis also inductively includes theparents. The display of events on plots may be correct for the definedsort gate, but rendering plots of populations for parent nodes withoutan express sort electronics decision may include misclassification dueto, for example, the in/out effects discussed. Including the parents byinduction solves this. The use of the well coordinates solves the wellordering requirement of the data and the need to calculate and trackstate changes between wells (e.g., where within the data stream did thenext well begin sorting).

Furthermore, the method 400 may improve the defining and analysis ofcomplex computational gates using algorithms other than coordinatecomparisons such as bitmaps. For example, a complex hierarchy may bereduced to an approximation using index sort bitmaps. Thus, the method400 may selectively apply complex computational aspects for events notsorted, but the sorted classification, whether expressly specified orinductively identified, to override.

As another example, given the coordinates transmitted with the otherevent measurements and sort decisions from, for example, the cellsorting device, a hierarchy of sort gates that were active for aparticular well sort can be identified. The method can replace theworkstation defined classification with the cell sorting device sortdecision across that hierarchy. For child gates sharing common ancestorswith the sort gate for the well, the method may replace the ancestorswith the sort decision from the cell sorter and for the remainingevents, rely on the software classification for the remaining parts ofthe child gates hierarchy tree.

The method 400 may begin at block 402. At block 402 it may be assumedthat a set of sort gates have been defined for a plate. The definitionmay be based on a preset sort for a sample or target cell of interest.At block 404, event data for a sample may be received. The event datamay be received directly from a cell sorter or other flow cytometrydevice. In some implementations, the event data may be received from astorage medium such as a memory device or data store. The event data mayinclude specific parameter values for each particle detected in thesample as well as sorting information generated by the sort electronics.

At block 406, a set of distinct sort gates used on a plate into whichthe sample was sorted is identified. The set of distinct sort gates maybe identified based on the event data. For example, the event data mayinclude an assay or sample identifier. Based on this identifier, thesort gates applied may be retrieved from a data store. A distinct sortgate may be a composite of two or more sort gates included in ahierarchy such as that shown in FIG. 1. For example, the hierarchy mayinclude a sort gate for singlets and, under singlets, a sort gate forcells associated with parameter 1. In such instances, two unique sortgates would be included in the set, one for singlets with parameter 1and a second for singles without parameter 1. The hierarchy of gates maybe traversed from leaf nodes to the root node for each unique sort gate.No entry in the set will be made if multiple sort gates share the sameparent gate.

At block 408, an index sort inclusion bitmap may be generated for eachsort gate included in the set of distinct sort gates. The inclusionbitmap may be used to uniquely identify each of the distinct sort gates.For example, if an event is within a gate within the hierarchy, it isalso within the parents of the gate within the hierarchy. Thus, theinclusion bitmaps may be generated inclusion bitmaps for the parents ofa sort gate to provide the inductive sort logic represented by thehierarchy. Consider the example gate hierarchy shown in Listing 1.

LISTING 1 A |--> B | |--> C | |--> D |--> E | |--> F

If, in the example hierarchy of Listing 1, sorting is performed on gateC then any event that is classified as within gate C is also within gateA and gate B. Gate D may represent some other measurement expressed bysome of the ‘C’ events. Although the sort electronics may not expresslysort events in population associated with gate D, the workstation caninfer that the sorting electronics thinks such events are within gate B.Thus the measurement and presentation of events vis-à-vis the actualsorted plate may be more accurate even though it is the workstation andnot the cell sorter that identified the classification for events ingate D.

At block 410, each well assigned in the plate may be assigned a sortgate. The assignment may include associating a specific sort gate with aspecific well in the plate.

At block 412, the relationship between the well and the sort gate may bepersisted by combining the well location to the index sort inclusiondata structure (e.g., bitmap). In some implementations, the welllocation may be specified using a specific portion of the index sortinclusion data structure such as specific bits of the bitmap. Forexample, assuming the plate is a 6×8 well plate and there are fourdistinct sort gates, Table 1 provides an example of bitmaps that may beused to represent the index sort inclusion data. The full bitmap may beformed by assigning the first two bits to a binary code for the sortgate and assigning the last six bits to represent a well on the plate.

TABLE 1 Sort Gate Binary Code Well Binary Code Full Bitmap 0 00 1A000000 00000000 1 01 2B 001010 01001010 2 10 3C 010011 10010011 3 11 4D100110 11100110

At block 414, the workstation may generate a classification of an eventincluded in the event data. The classification may be generated based atleast in part on a parameter value for the event and the gate. Forexample, if the gate defines a population of events having a range of x,y parameter values, the classification for the event may be based onwhether the parameter value for the event falls within the range. Anevent may be classified into one or more groups such as all particles,singlets, or specific parameter groups.

At block 416, a determination is made as to whether the classificationgenerated at block 414 is associated with the gate. If theclassification is not associated with the gate, then there may be nodiscrepancy between the sorting performed by the sorting electronics andthe sorting generated by the workstation based on the event data. Insuch instances, the method 400 may end at block 490.

If the determination at block 416 is affirmative, then it is possiblethat classification for the event is associated with a gate defining asort population. At block 418, a determination may be made as to whetherevent sorting information received from the flow cytometer correspondsto the gate associated with the classification generated for the eventat block 414. If not, then there may be no discrepancy between thesorting performed by the sorting electronics and the sorting generatedby the workstation based on the event data. In such instances, themethod 400 may end at block 490.

If the determination at block 418 is affirmative, the particleassociated with the event was sorted differently by the sortingelectronics than by the workstation. In such instance, the method 400may, at block 420, override the classification generated by theworkstation with an alternate classification. The alternateclassification may be associated with the well location identified bythe index sort inclusion bit map. That is, the workstation will honorthe actual sorting performed by the soring electronics rather than thesorting generated by the workstation based on the parameter event datafor the particle. The overriding may include updating a record stored inmemory for the cell or particle associated with the event data. Thischange may be reflected in the summary panel or other part of the userinterface 200 to provide an accurate visualization of the event databased on the sorting actually performed by the sorting electronics.Thus, the sort decision information is fused with the workstationinformation. The method may then end at block 490. It will be understoodthat all or a portion of the method 400 may be repeated for fusing sortdecision information for other events via the workstation. For example,an experiment may include hundreds of thousands of particles, eachassociated with multiple parameters which may be used to sort accordingto a complex hierarchy of gates. Accordingly, aspects of the method 400may be repeated to classify other events or the same event according toa different set of gates.

Gating may be performed on workstation software at a higher precisionthan can be achieved on the instrument hardware. For example,bi-exponential transformation may not be supported, such as by theBecton, Dickinson FACSMelody™ hardware, but may be supported by theinstrument software so the software approximates the gating bytransforming coordinates for the hardware but this may not provide adesired level of precision. Higher precision may be desirable but cancause a problem when index sorting as there may be some smalldisagreement around gating e.g. hardware uses a lower precision bitmapthat has edge effects. The innovative features combine sort decisioninformation with the workstation software gating to reconstruct as muchof the event classification as possible from the hardware.

The features provide several non-limiting advantages. For example, thefeatures do not require an exact reproduction of hardware eventclassification within software. This can be desirable because theelectronics platforms may be different versus a workstation CPU andthere are many areas where error may be introduced. As another example,the features do not require the separation of the recorded data streamby well. The data does not need to be guaranteed to be in temporalorder. Using the well index to look up into a bitmap to see if the gateis applicable to that well does not require analysis state changes orcomplex conditional logic in computing gate membership.

Exemplary Environments

FIG. 5 shows a functional block diagram for one example of a graphicscontrol system for that may implement one or more of the featuresdescribed.

A flow cytometer 502 may be configured to acquire flow cytometricevents. For example, flow cytometer 502 may generate flow cytometricevent data. The flow cytometer 502 may be configured to provide flowcytometric events to the graphics controller 590. A data communicationchannel may be included between the flow cytometer 502 and the graphicscontroller 590. The flow cytometric events may be provide to thegraphics controller 590 via the data communication channel.

The graphics controller 590 may be configured to receive flow cytometricevents from the flow cytometer 502. The flow cytometric events receivedfrom the flow cytometer 502 may include flow cytometric event data. Thegraphics controller 590 may be configured to provide a graphical displayincluding a first plot or other visualization (e.g., wells) of flowcytometric events to a display device 506. The graphics controller 590may be further configured to render a gate around a population of flowcytometric events shown by the display device 506, overlaid upon thefirst plot. Additionally, the graphics controller 590 may be furtherconfigured to display the flow cytometric events on the display device506 within the gate differently from other events in the flow cytometricevents outside of the gate. For example, the graphics controller 590 maybe configured to render the color of flow cytometric events containedwithin the gate to be distinct from the color of flow cytometric eventsoutside of the gate. The display device 506 may be implemented as amonitor, a tablet computer, a smartphone, or other electronic deviceconfigured to present graphical interfaces.

The graphics controller 590 may be configured to receive a selectionsignals identifying activation of a control element such as a button,drawing of a gate, or keyboard input from a first input device. Forexample, the input device may be implemented as a mouse 510. The mouse510 may initiate a gate selection signal to the graphics controller 590identifying the gate to be displayed on or manipulated via the displaydevice 506 (e.g., by clicking on or in the desired gate when the cursoris positioned there). If the visualizations include a well display,selection of particular wells may be included in the gate selectionsignal.

The first and second input devices may be implemented as one or more ofthe mouse 510, a keyboard 508, or other means for providing an inputsignal to the graphics controller 590 such as a touchscreen, a stylus,an optical detector, or a voice recognition system. Some input devicesmay include multiple inputting functions. In such implementations, theinputting functions may each be considered an input device. For example,as shown in FIG. 5, the mouse 510 may include a right mouse button and aleft mouse button, each of which may generate a triggering event.

The triggering event may cause the graphics controller 590 to alter themanner in which the data is displayed or which portions of the data isactually displayed on the display device 506 or both at the same time.

In some embodiments, the graphics controller 590 may be configured todetect when gate selection is initiated by the mouse 510. The graphicscontroller 590 may be further configured to automatically modify one ormore interface elements to respond to the selection/input as described.The alteration may include loading event data from a specified sourceand presenting a user interface, such as the user interface 200 shown inFIG.2 based at least in part on fused sort information.

The graphics controller 590 may be connected to a storage device 504.The storage device 504 may be configured to receive and store flowcytometric events from the graphics controller 590. The storage device504 may also be configured to receive and store flow cytometric eventdata from the graphics controller 590. The storage device 504 may befurther configured to allow retrieval of flow cytometric events and flowcytometric event data by the graphics controller 590.

A display device 506 may be configured to receive display data from thegraphics controller 590. The display data may comprise plots of flowcytometric events and gates outlining sections of the plots. The displaydevice 506 may be further configured to alter the information presentedaccording to input received from the graphics controller 590 inconjunction with input from the flow cytometer 502, the storage device504, the keyboard 508, and/or the mouse 510.

Further Embodiments

Aspects of the description focus on flow cytometers and flow cytometryevent data. In some embodiments, the event data may correspond to otherquantitative biological data indicating expression of a particularprotein or gene. For example, the event data may indicate the presenceof an mRNA sequence within a cell or across a mixed population of cells.The event data may identify an absolute number of gene transcripts of atranscriptome for a cell or cells. Presentation of the event data may beadjusted per cell or per gene expression to provide differentperspectives on populations of event data of particular interest (e.g.,associated with a particular mRNA sequence, taken from a specific cell,etc.). The event data may be generated using massively parallel singlecell analytic features such as those described in U.S. Pat. No.9,567,645 which is hereby incorporated by reference in its entirety. Onecommercially available single-cell analysis system is the Becton,Dickinson Rhapsody™ hardware by Becton, Dickinson and Company ofFranklin Lakes, N.J. The features discussed may be applied to reconcilesorting decisions based on gene expression.

As used herein, the terms “determine” or “determining” encompass a widevariety of actions. For example, “determining” may include calculating,computing, processing, deriving, investigating, looking up (e.g.,looking up in a table, a database or another data structure),ascertaining and the like. Also, “determining” may include receiving(e.g., receiving information), accessing (e.g., accessing data in amemory) and the like. Also, “determining” may include resolving,selecting, choosing, establishing, and the like.

As used herein, the terms “provide” or “providing” encompass a widevariety of actions. For example, “providing” may include storing a valuein a location for subsequent retrieval, transmitting a value directly tothe recipient, transmitting or storing a reference to a value, and thelike. “Providing” may also include encoding, decoding, encrypting,decrypting, validating, verifying, and the like.

As used herein, the term “selectively” or “selective” may encompass awide variety of actions. For example, a “selective” process may includedetermining one option from multiple options. A “selective” process mayinclude one or more of: dynamically determined inputs, preconfiguredinputs, or user-initiated inputs for making the determination. In someimplementations, an n-input switch may be included to provide selectivefunctionality where n is the number of inputs used to make theselection.

As used herein, the term “message” encompasses a wide variety of formatsfor communicating (e.g., transmitting or receiving) information. Amessage may include a machine readable aggregation of information suchas an XML document, fixed field message, comma separated message, or thelike. A message may, in some implementations, include a signal utilizedto transmit one or more representations of the information. Whilerecited in the singular, it will be understood that a message may becomposed, transmitted, stored, received, etc. in multiple parts.

As used herein a “user interface” (also referred to as an interactiveuser interface, a graphical user interface, an interface, or a UI) mayrefer to a network based interface including data fields and/or othercontrols for receiving input signals or providing electronic informationand/or for providing information to the user in response to any receivedinput signals. A UI may be implemented in whole or in part usingtechnologies such as hyper-text mark-up language (HTML), ADOBE® FLASH®,JAVA®, MICROSOFT® .NET®, web services, and rich site summary (RSS). Insome implementations, a UI may be included in a stand-alone client (forexample, thick client, fat client) configured to communicate (e.g., sendor receive data) in accordance with one or more of the aspectsdescribed.

As used herein, “system,” “instrument,” “apparatus,” and “device”generally encompass both the hardware (e.g., mechanical and electronic)and, in some implementations, associated software (e.g., specializedcomputer programs for graphics control) components.

As used herein, an “event” generally refers to the data measured from asingle particle, such as cells or synthetic particles. Typically, thedata measured from a single particle include a number of parameters,including one or more light scattering parameters, and at least onefluorescence intensity parameters. Thus, each event is represented as avector of parameter measurements, wherein each measured parametercorresponds to one dimension of the data space. In some biologicalapplications, event data may correspond to quantitative biological dataindicating expression of a particular protein or gene.

As used herein, a “population”, or “subpopulation” of particles, such ascells or other particles, generally refers to a group of particles thatpossess optical properties with respect to one or more measuredparameters such that measured parameter data form a cluster in the dataspace. Thus, populations are recognized as clusters in the data.Conversely, each data cluster generally is interpreted as correspondingto a population of a particular type of cell or particle, althoughclusters that correspond to noise or background typically also areobserved. A cluster may be defined in a subset of the dimensions, e.g.,with respect to a subset of the measured parameters, which correspondsto populations that differ in only a subset of the measured parameters.

As used herein, a “gate” generally refers to a boundary identifying asubset of data of interest. In cytometry, a gate may bound a group ofevents of particular interest. The group of events may be referred to apopulation. Further, as used herein, “gating” may generally refer to theprocess of defining a gate for a given set of data such as a via a userinterface or plate and well selections.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

Those of skill in the art would understand that information, messages,and signals may be represented using any of a variety of differenttechnologies and techniques. For example, data, instructions, commands,information, signals, bits, symbols, and chips that may be referencedthroughout the above description may be represented by voltages,currents, electromagnetic waves, magnetic fields or particles, opticalfields or particles, or any combination thereof.

Those of skill in the art would further appreciate that the variousillustrative logical blocks, modules, circuits, and algorithm stepsdescribed in connection with the embodiments disclosed herein may beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, circuits,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. Skilled artisans may implement thedescribed functionality in varying ways for each particular application,but such implementation decisions should not be interpreted as causing adeparture from the scope of the present invention.

The techniques described herein may be implemented in hardware,software, firmware, or any combination thereof. Such techniques may beimplemented in any of a variety of devices such as specificallyprogrammed event processing computers, wireless communication devices,or integrated circuit devices. Any features described as modules orcomponents may be implemented together in an integrated logic device orseparately as discrete but interoperable logic devices. If implementedin software, the techniques may be realized at least in part by acomputer-readable data storage medium comprising program code includinginstructions that, when executed, performs one or more of the methodsdescribed above. The computer-readable data storage medium may form partof a computer program product, which may include packaging materials.The computer-readable medium may comprise memory or data storage media,such as random access memory (RAM) such as synchronous dynamic randomaccess memory (SDRAM), read-only memory (ROM), non-volatile randomaccess memory (NVRAM), electrically erasable programmable read-onlymemory (EEPROM), FLASH memory, magnetic or optical data storage media,and the like. The computer-readable medium may be a non-transitorystorage medium. The techniques additionally, or alternatively, may berealized at least in part by a computer-readable communication mediumthat carries or communicates program code in the form of instructions ordata structures and that can be accessed, read, and/or executed by acomputing device, such as propagated signals or waves.

The program code may be executed by a specifically programmed graphicsprocessor, which may include one or more processors, such as one or moredigital signal processors (DSPs), configurable microprocessors, anapplication specific integrated circuits (ASICs), field programmablelogic arrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Such a graphics processor may be specially configured toperform any of the techniques described in this disclosure. Acombination of computing devices, e.g., a combination of a DSP and amicroprocessor, a plurality of microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration in at least partial data connectivity may implement one ormore of the features describe. Accordingly, the term “processor,” asused herein may refer to any of the foregoing structure, any combinationof the foregoing structure, or any other structure or apparatus suitablefor implementation of the techniques described herein. In addition, insome aspects, the functionality described herein may be provided withindedicated software modules or hardware modules configured for encodingand decoding, or incorporated in a specialized graphic control card.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

What is claimed is:
 1. A computer-implemented method of fused gating forindex sorting, the method comprising: under control of one or moreelectronic processors: receiving, at a workstation from a memory,experiment information identifying: (i) a first sort gate, (ii) a firstplate destination for particles identified within the first sort gate ona collection plate, (iii) a second sort gate, and (iv) a second platedestination for particles identified within the second sort gate on thecollection plate; generating, at the workstation, a first index sortinclusion bitmap identifying first plate locations on the collectionplate using the first sort gate; generating, at the workstation, asecond index sort inclusion bitmap identifying second plate locations onthe collection plate using the second sort gate; receiving, at theworkstation, event data for the particles, the event data including foreach particle a fluorescent parameter value and a sort decision of asorting device; determining, via the workstation, that an individualfluorescent parameter value for an individual particle included in theparticles is within the first sort gate; associating the individualparticle with the first sort gate for presentation via a user interface;determining, via the workstation, that an individual sort decision forthe individual particle corresponds to the second sort gate based atleast in part on a comparison between the individual sort decision andthe second index sort inclusion bitmap; and associating the individualparticle with the second sort gate for presentation via the userinterface.
 2. The computer-implemented method of claim 1, wherein thesort decision is generated by a sorting device configured to sort theparticles into the collection plate based at least in part on theexperiment information.
 3. The computer-implemented method of claim 1,further comprising: receiving, at the workstation, a geometric shapedefining the first sort gate, wherein the geometric shape is pixelated.4. The computer-implemented method of claim 1, further comprising:receiving, at the workstation, a request to adjust an axis for ageometric shape defining the first sort gate, wherein the geometricshape, after the axis is adjusted, is pixelated.
 5. Thecomputer-implemented method of claim 4, wherein the request includes abinomial transformation to adjust the axis.
 6. The computer-implementedmethod of claim 1, wherein the first sort gate identifies a set ofparticles having parameter values distinct from another set of particlesidentified by the second sort gate.
 7. An apparatus comprising: aparticle analyzer configured to acquire quantitative biological eventdata; one or more processors in communication with the particle analyze,the one or more processors configured to: receive, from a memory,experiment information identifying: (i) a first sort gate, (ii) a firstplate destination for particles identified within the first sort gate,(iii) a second sort gate, and (iv) a second plate destination forparticles identified within the second sort gate; generate a first indexsort inclusion bitmap uniquely identifying the first sort gate; generatea second index sort inclusion bitmap uniquely identifying and the secondsort gate; receive, from the particle analyzer, event data for theparticles, the event data including for each particle a fluorescentparameter value and a sort decision; determine that an individualfluorescent parameter value for an individual particle included in theparticles is within the first sort gate; associate the individualparticle with the first sort gate for presentation via a user interface;determine that an individual sort decision for the individual particlecorresponds to the second sort gate based at least in part on acomparison between the individual sort decision and the second indexsort inclusion bitmap; and associate the individual particle with thesecond sort gate for presentation via the user interface.
 8. Theapparatus of claim 7, wherein the sort decision is generated by theparticle analyzer, wherein the particle analyzer is configured to sortthe particles into the plate based at least in part on the experimentinformation.
 9. The apparatus of claim 7, wherein the one or moreprocessors are further configured to: receive a geometric shape definingthe first sort gate, wherein the geometric shape is pixelated.
 10. Theapparatus of claim 7, wherein the one or more processors are furtherconfigured to: receive a request to adjust an axis for a geometric shapedefining the first sort gate, wherein the geometric shape, after theaxis is adjusted, is pixelated.
 11. The apparatus of claim 10, whereinthe request includes a non-linear transformation to adjust the axis. 12.The apparatus of claim 7, wherein the first sort gate identifies a setof particles having parameter values distinct from another set ofparticles identified by the second sort gate.
 13. The apparatus of claim7, wherein the one or more processors are further configured to: causedisplay of a graphic representation of the plate, the graphicrepresentation of the plate including a first area representing thesecond plate destination; and cause display of a graphic indicator forthe individual particle within the first area.
 14. A computer-readablemedium having stored thereon instructions which when executed by aprocessor of a device, cause the device to at least: receive, from amemory, experiment information identifying: (i) a first sort gate, (ii)a first plate destination for particles identified within the first sortgate, (iii) a second sort gate, and (iv) a second plate destination forparticles identified within the second sort gate; generate a first indexsort inclusion value uniquely identifying the first sort gate; generatea second index sort inclusion value uniquely identifying and the secondsort gate; receive, from a particle analyzer, event data for theparticles, the event data including for each particle a fluorescentparameter value and a sort decision; determine that an individualfluorescent parameter value for an individual particle included in theparticles is within the first sort gate; associate the individualparticle with the first sort gate for presentation via a user interface;determine that an individual sort decision for the individual particlecorresponds to the second sort gate based at least in part on acomparison between the individual sort decision and the second indexsort inclusion value; and associate the individual particle with thesecond sort gate for presentation via the user interface.
 15. Thecomputer-readable medium of claim 14, wherein the sort decision isgenerated by a sorting device configured to sort the particles into theplate based at least in part on the experiment information.
 16. Thecomputer-readable medium of claim 14, further having stored thereoninstructions which when executed by the processor of the device, causethe device to at least to: receive a geometric shape defining the firstsort gate, wherein the geometric shape is pixelated.
 17. Thecomputer-readable medium of claim 14, further having stored thereoninstructions which when executed by the processor of the device, causethe device to at least to: receive a request to adjust an axis for ageometric shape defining the first sort gate, wherein the geometricshape, after the axis is adjusted, is pixelated.
 18. Thecomputer-readable medium of claim 17, wherein the request includes anon-linear transformation to adjust the axis.
 19. The computer-readablemedium of claim 14, wherein the first sort gate identifies a set ofparticles having parameter values distinct from another set of particlesidentified by the second sort gate.
 20. The computer-readable medium ofclaim 14, further having stored thereon instructions which when executedby the processor of the device, cause the device to at least to: causedisplay of presentation of a graphic representation of the plate, thegraphic representation of the plate including a first area representingthe second plate destination; and cause presentation of a graphicindicator for the individual particle within the first area.